Data sources, having named properties, can be organized into ontologies in many different ways. For example, a particular data record may have separate categories or entries for a person's name, address, and other information about that individual, while a second data source may store related information about that same individual, but may organize that data using different categories, such as separation by first name, last name, street, city, and state address, etc. Furthermore, such data may be distributed across a data source, for example across various different tables of a database. Identifying relationships between such data sources, and the associated ontologies represented by those data sources, can be complex.
In particular cases, semantically linking large real-world ontologies populated with entities from heterogeneous sources, problems arise. For example, several entities in different ontologies are expected to be related to each other but not necessarily with one of the typical relationships (e.g., equivalent-to, is-a, part-of, subsumed-by). Intuitively, there are entities that are related since they share a certain amount of mutual information. However, most of the existing systems for ontology matching focus on computing the specific relations of equivalence and subsumption, and as such, may miss entities that are related by some other relationship. Accordingly, improvements in ontology matching, and particular in developing semantic links between real-world ontologies, are desirable.